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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

From bulk to surface: Structure and dynamics of amorphous alumina from deep potential molecular dynamics

Understanding the atomic-scale structure and dynamics of amorphous oxide surfaces is essential for interpreting their chemical reactivity, mechanical stability, and interfacial behavior, yet direct experimental characterization remains challenging. We employ Deep Potential (DP) molecular dynamics to generate large-scale, ab initio -quality models of amorphous Al 2 O 3 bulk glasses and melt-quenched free surfaces, enabling a quantitative analysis of both structure and relaxation dynamics with statistical confidence inaccessible to direct ab initio simulation. The trained DP model reproduces experimental liquid and glass structure, captures the cooling-rate dependence of the bulk glass transition, and corrects systematic biases in the polyhedral populations predicted by widely used classical force fields. At the free surface, mass density recovers to bulk values over ~10 Å, while local coordination requires a slightly wider subsurface region to fully converge. The outermost layer is oxygen-enriched, exhibits altered polyhedral connectivity with contracted Al–O bonds, and hosts a broad population of under-coordinated motifs (notably AlO 3 and OAl 2 ) whose abundances are governed by glass stability. These under-coordinated surface motifs exhibit distinct vibrational signatures and occur as locally paired Lewis acid and Brønsted base sites consistent with bond-valence compensation, yet remain spatially dispersed rather than aggregating into extended clusters. Despite this pronounced structural heterogeneity, surface relaxation and the glass-transition temperature remain comparable to their bulk counterparts, suggesting that the disordered surface is kinetically stable once formed. Together, these results establish a molecular-level picture of amorphous alumina surfaces and demonstrate the capability of machine-learned potentials to resolve structure–property relationships in disordered oxide interfaces.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Detection of significant antiviral drug effects on COVID-19 with reasonable sample sizes in randomized controlled trials: A modeling study

Development of an effective antiviral drug for Coronavirus Disease 2019 (COVID-19) is a global health priority. Although several candidate drugs have been identified through in vitro and in vivo models, consistent and compelling evidence from clinical studies is limited. The lack of evidence from clinical trials may stem in part from the imperfect design of the trials. We investigated how clinical trials for antivirals need to be designed, especially focusing on the sample size in randomized controlled trials. A modeling study was conducted to help understand the reasons behind inconsistent clinical trial findings and to design better clinical trials. We first analyzed longitudinal viral load data for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) without antiviral treatment by use of a within-host virus dynamics model. The fitted viral load was categorized into 3 different groups by a clustering approach. Comparison of the estimated parameters showed that the 3 distinct groups were characterized by different virus decay rates (p-value < 0.001). The mean decay rates were 1.17 d -1 (95% CI: 1.06 to 1.27 d -1 ), 0.777 d -1 (0.716 to 0.838 d -1 ), and 0.450 d -1 (0.378 to 0.522 d -1 ) for the 3 groups, respectively. Such heterogeneity in virus dynamics could be a confounding variable if it is associated with treatment allocation in compassionate use programs (i.e., observational studies). Subsequently, we mimicked randomized controlled trials of antivirals by simulation. An antiviral effect causing a 95% to 99% reduction in viral replication was added to the model. To be realistic, we assumed that randomization and treatment are initiated with some time lag after symptom onset. Using the duration of virus shedding as an outcome, the sample size to detect a statistically significant mean difference between the treatment and placebo groups (1:1 allocation) was 13,603 and 11,670 (when the antiviral effect was 95% and 99%, respectively) per group if all patients are enrolled regardless of timing of randomization. The sample size was reduced to 584 and 458 (when the antiviral effect was 95% and 99%, respectively) if only patients who are treated within 1 day of symptom onset are enrolled. We confirmed the sample size was similarly reduced when using cumulative viral load in log scale as an outcome. We used a conventional virus dynamics model, which may not fully reflect the detailed mechanisms of viral dynamics of SARS-CoV-2. The model needs to be calibrated in terms of both parameter settings and model structure, which would yield more reliable sample size calculation. In this study, we found that estimated association in observational studies can be biased due to large heterogeneity in viral dynamics among infected individuals, and statistically significant effect in randomized controlled trials may be difficult to be detected due to small sample size. The sample size can be dramatically reduced by recruiting patients immediately after developing symptoms. We believe this is the first study investigated the study design of clinical trials for antiviral treatment using the viral dynamics model.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Forecasting the potential of weak lensing magnification to enhance LSST large-scale structure analyses

ABSTRACT Recent works have shown that weak lensing magnification must be included in upcoming large-scale structure analyses, such as for the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), to avoid biasing the cosmological results. In this work, we investigate whether including magnification has a positive impact on the precision of the cosmological constraints, as well as being necessary to avoid bias. We forecast this using an LSST mock catalogue and a halo model to calculate the galaxy power spectra. We find that including magnification has little effect on the precision of the cosmological parameter constraints for an LSST galaxy clustering analysis, where the halo model parameters are additionally constrained by the galaxy luminosity function. In particular, we find that for the LSST gold sample (i < 25.3) including weak lensing magnification only improves the galaxy clustering constraint on Ωm by a factor of 1.03, and when using a very deep LSST mock sample (i < 26.5) by a factor of 1.3. Since magnification predominantly contributes to the clustering measurement and provides similar information to that of cosmic shear, this improvement would be reduced for a combined galaxy clustering and shear analysis. We also confirm that not modelling weak lensing magnification will catastrophically bias the cosmological results from LSST. Magnification must therefore be included in LSST large-scale structure analyses even though it does not significantly enhance the precision of the cosmological constraints.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

On the scale of heterogeneity in composite electrodes of batteries

An electrode in cylindrical or pouch cell batteries contains millions of active particles embedded in a conductive network. Battery performance, such as voltage, capacity, and cyclic efficiency, is a collective response of the particle network. We use optical microscopy to measure the local, heterogeneous state of charge of individual particles upon charging and discharging. The optical reflectivity is proportional to Li composition in the ternary oxide LiNi x Mn y Co z O 2 (NMC) cathode. Through clustering analysis, we determine the scale of heterogeneity where a representative volume in the composite electrode contains 100 to 1,000 particles. The heterogeneous activity in the particle network can be described by Weibull defect population at the particle interface with the conductive matrix.

batteries↗
DOE OSTI2020

Parallel algorithms for hyperdynamics and local hyperdynamics

Hyperdynamics (HD) is a method for accelerating the timescale of standard molecular dynamics (MD). It can be used for simulations of systems with an energy potential landscape that is a collection of basins, separated by barriers, where transitions between basins are infrequent. HD enables the system to escape from a basin more quickly while enabling a statistically accurate renormalization of the simulation time, thus effectively boosting the timescale of the simulation. In [Kim, Perez, Voter, J Chem Phys, 139:144110, 2013)1, a local version of HD was formulated, which exploits the intrinsic locality characteristic typical of most systems to mitigate the poor scaling properties of standard HD as the system size is increased. In this paper, we discuss how both HD and local HD can be formulated to run efficiently in parallel. We have implemented these ideas in the LAMMPS MD code, which means HD can be used with any interatomic potential LAMMPS supports. Together, these parallel methods allow simulations of any size to achieve the time acceleration offered by HD (which can be orders of magnitude), at a cost 3-5x that of standard MD. As examples, we performed two simulations of a million-atom system to model the diffusion and clustering of Pt adatoms on a large patch of Pt(100) surface for 80 and 160 μs.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

Idealized simulation study of the relationship of disdrometer sampling statistics with the precision of precipitation rate measurement

Due to the discretized nature of rain, the measurement of a continuous precipitation rate by disdrometers is subject to statistical sampling errors. Here, Monte Carlo simulations are employed to obtain the precision of rain detection and rate as a function of disdrometer collection area and compared with World Meteorological Organization guidelines for a 1 min sample interval and 95 % probability. To meet these requirements, simulations suggest that measurements of light rain with rain rates R ≤ 0.50 mm h-1 require a collection area of at least 6 cm × 6 cm, and for R = 1 mm h -1 , the minimum collection area is 13 cm × 13 cm. For R = 0.01 mm h -1 , a collection area of 2 cm × 2 cm is sufficient to detect a single drop. Simulations are compared with field measurements using a new hotplate device, the Differential Emissivity Imaging Disdrometer. The field results suggest an even larger plate may be required to meet the stated accuracy, likely in part due to non-Poissonian hydrometeor clustering.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Utilizing Essential Symmetry Breaking in Auxiliary-Field Quantum Monte Carlo: Application to the Spin Gaps of the C 36 Fullerene and an Iron Porphyrin Model Complex

We present three distinct examples where phaseless auxiliary-field quantum Monte Carlo (ph-AFQMC) can be reliably performed with a single-determinant trial wave function with essential symmetry breaking. Essential symmetry breaking was first introduced by Lee and Head-Gordon [ Phys. Chem. Chem. Phys. 2019, 21, 4763-4778, 10.1039/C8CP07613H]. We utilized essential complex and time-reversal symmetry breaking with ph-AFQMC to compute the triplet–singlet energy gap in the TS12 set. We found statistically better performance of ph-AFQMC with complex-restricted orbitals than with spin-unrestricted orbitals. We then showed the utilization of essential spin symmetry breaking when computing the singlet–triplet gap of a known biradicaloid, C 36 . ph-AFQMC with spin-unrestricted Hartree–Fock (ph-AFQMC+UHF) fails catastrophically even with spin-projection and predicts no biradicaloid character. With approximate Brueckner orbitals obtained from regularized orbital-optimized second-order Møller–Plesset perturbation theory (κ-OOMP2), ph-AFQMC quantitatively captures strong biradicaloid character of C 36 . Lastly, we applied ph-AFQMC to the computation of the quintet–triplet gap in a model iron porphyrin complex where brute-force methods with a small active space fail to capture the triplet ground state. We show unambiguously that neither triplet nor quintet is strongly correlated using UHF, κ-OOMP2, and coupled-cluster with singles and doubles (CCSD) performed on UHF and κ-OOMP2 orbitals. There is no essential symmetry breaking in this problem. By virtue of this, we were able to perform UHF+ph-AFQMC reliably with a cc-pVTZ basis set and predicted a triplet ground state for this model geometry. The largest ph-AFQMC in this work correlated 186 electrons in 956 orbitals. Our work highlights the utility, scalability, and accuracy of ph-AFQMC with a single-determinant trial wave function with essential symmetry breaking for systems mainly dominated by dynamical correlation with little static correlation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

A review on recent machine learning applications for imaging mass spectrometry studies

Imaging mass spectrometry (IMS) is a powerful analytical technique widely used in biology, chemistry, and materials science fields that continue to expand. IMS provides a qualitative compositional analysis and spatial mapping with high chemical specificity. The spatial mapping information can be 2D or 3D depending on the analysis technique employed. Due to the combination of complex mass spectra coupled with spatial information, large high-dimensional datasets (hyperspectral) are often produced. Therefore, the use of automated computational methods for an exploratory analysis is highly beneficial. The fast-paced development of artificial intelligence (AI) and machine learning (ML) tools has received significant attention in recent years. These tools, in principle, can enable the unification of data collection and analysis into a single pipeline to make sampling and analysis decisions on the go. There are various ML approaches that have been applied to IMS data over the last decade. Here, in this review, we discuss recent examples of the common unsupervised (principal component analysis, non-negative matrix factorization, k-means clustering, uniform manifold approximation and projection), supervised (random forest, logistic regression, XGboost, support vector machine), and other methods applied to various IMS datasets in the past five years. The information from this review will be useful for specialists from both IMS and ML fields since it summarizes current and representative studies of computational ML-based exploratory methods for IMS.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Optimal dimensionality selection for independent component analysis of transcriptomic data

Independent component analysis is an unsupervised machine learning algorithm that separates a set of mixed signals into a set of statistically independent source signals. Applied to high-quality gene expression datasets, independent component analysis effectively reveals both the source signals of the transcriptome as co-regulated gene sets, and the activity levels of the underlying regulators across diverse experimental conditions. Two major variables that affect the final gene sets are the diversity of the expression profiles contained in the underlying data, and the user-defined number of independent components, or dimensionality, to compute. Availability of high-quality transcriptomic datasets has grown exponentially as high-throughput technologies have advanced; however, optimal dimensionality selection remains an open question. We computed independent components across a range of dimensionalities for four gene expression datasets with varying dimensions (both in terms of number of genes and number of samples). We computed the correlation between independent components across different dimensionalities to understand how the overall structure evolves as the number of user-defined components increases. We then measured how well the resulting gene clusters reflected known regulatory mechanisms, and developed a set of metrics to assess the accuracy of the decomposition at a given dimension. We found that over-decomposition results in many independent components dominated by a single gene, whereas under-decomposition results in independent components that poorly capture the known regulatory structure. From these results, we developed a new method, called OptICA, for finding the optimal dimensionality that controls for both over- and under-decomposition. Specifically, OptICA selects the highest dimension that produces a low number of components that are dominated by a single gene. We show that OptICA outperforms two previously proposed methods for selecting the number of independent components across four transcriptomic databases of varying sizes. OptICA avoids both over-decomposition and under-decomposition of transcriptomic datasets resulting in the best representation of the organism’s underlying transcriptional regulatory network.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Comparing galaxy formation in the L-GALAXIES semi-analytical model and the IllustrisTNG simulations

ABSTRACT We perform a comparison, object by object and statistically, between the Munich semi-analytical model, L-GALAXIES, and the IllustrisTNG hydrodynamical simulations. By running L-GALAXIES on the IllustrisTNG dark matter-only merger trees, we identify the same galaxies in the two models. This allows us to compare the stellar mass, star formation rate, and gas content of galaxies, as well as the baryonic content of subhaloes and haloes in the two models. We find that both the stellar mass functions and the stellar masses of individual galaxies agree to better than ${\sim} 0.2\,$dex. On the other hand, specific star formation rates and gas contents can differ more substantially. At z = 0, the transition between low-mass star-forming galaxies and high-mass quenched galaxies occurs at a stellar mass scale ${\sim} 0.5\,$dex lower in IllustrisTNG than that in L-GALAXIES. IllustrisTNG also produces substantially more quenched galaxies at higher redshifts. Both models predict a halo baryon fraction close to the cosmic value for clusters, but IllustrisTNG predicts lower baryon fractions in group environments. These differences are primarily due to differences in modelling feedback from stars and supermassive black holes. The gas content and star formation rates of galaxies in and around clusters and groups differ substantially, with IllustrisTNG satellites less star forming and less gas rich. We show that environmental processes such as ram-pressure stripping are stronger and operate to larger distances and for a broader host mass range in IllustrisTNG. We suggest that the treatment of galaxy evolution in the semi-analytic model needs to be improved by prescriptions that capture local environmental effects more accurately.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Elucidating ion capture and transport mechanisms of Preyssler anions in aqueous solutions using biased MACE-accelerated MD simulations

Equilibrium and biased multi-atomic cluster expansion (MACE) accelerated molecular dynamics (MD) simulations in aqueous solutions are performed to investigate the ion capture and transport mechanisms of the {P 5 W 30 } Preyssler anion (PA) as the smallest representative member of the extended polyoxometalate (POM) family with an internal cavity. The unique interatomic interactions present in the internal cavity vs the exterior of PA are carefully investigated using equilibrium MACE MD simulations for two representative Na(H 2 O)@PA and Na@PA complexes in aqueous solutions. Our careful analyses of radial distribution functions and coordination numbers show that the presence of confined water in Na(H 2 O)@PA has profound modulating effects on the nature of the interactions of the encapsulated ion with the oxygens of the PA cavity. Using well-converged MACE-accelerated multiple walker well-tempered metadynamics simulations with nanosecond timescales, two different associative (ion exchange) and dissociative (ion ejection) ion transport mechanisms were carefully investigated for Na + as one of the most abundant and representative ions present in seawater and saline solutions. By comparing systems with and without confined water, it was found that the presence of only one pre-encapsulated confined water in Na(H 2 O)@PA dramatically changes the free energy landscape of ion transport processes. It was also found that the contraction and dilation of the two windows present in PA directly influence the Na + and H 2 O transport. Furthermore, the results from this work are helpful, as they show a viable path toward tuning the ion exchange and transport phenomena in aqueous solutions of POM molecular clusters and frameworks.

25 ENERGY STORAGE↗
DOE OSTI2022

Clustering with general photo- z uncertainties: application to Baryon Acoustic Oscillations

ABSTRACT Photometric data can be analysed using the 3D correlation function ξp to extract cosmological information via e.g. measurement of the Baryon Acoustic Oscillations (BAO). Previous studies modeled ξp assuming a Gaussian photo-z approximation. In this work we improve the modeling by incorporating realistic photo-z distribution. We show that the position of the BAO scale in ξp is determined by the photo-z distribution and the Jacobian of the transformation. The latter diverges at the transverse scale of the separation s⊥, and it explains why ξp traces the underlying correlation function at s⊥, rather than s, when the photo-z uncertainty σz/(1+ z) ≳ 0.02. We also obtain the Gaussian covariance for ξp. Due to photo-z mixing, the covariance of ξp shows strong off-diagonal elements. The high correlation of the data causes some issues to the data fitting. None the less, we find that either it can be solved by suppressing the largest eigenvalues of the covariance or it is not directly related to the BAO. We test our BAO fitting pipeline using a set of mock catalogs. The data set is dedicated for Dark Energy Survey Year 3 (DES Y3) BAO analyses and includes realistic photo-z distributions. The theory template is in good agreement with mock measurement. Based on the DES Y3 mocks, ξp statistic is forecast to constrain the BAO shift parameter α to be 1.001 ± 0.023, which is well consistent with the corresponding constraint derived from the angular correlation function measurements. Thus, ξp offers a competitive alternative for the photometric data analyses.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Density weighted angular redshift fluctuations: a new cosmological observable

We propose the use of angular fluctuations in the galaxy redshift field as a new way to extract cosmological information in the Universe. This new probe $\delta z (\hat{\mathbf {n}})$ consists of the statistics of sky maps built by projecting redshifts under a Gaussian window of width σz centred upon a redshift zobs, and weighted by the galaxy density field. We compute the angular power spectrum of the $\delta z (\hat{\mathbf {n}})$ field in both numerical simulations and in linear perturbation theory. From these, we find that the $\delta z (\hat{\mathbf {n}})$ field (i) is sensitive to the underlying density and peculiar velocity fields; (ii) is highly correlated, at the $\gtrsim 60\, {{\ \rm per\ cent}}$ level, to the line-of-sight projected peculiar velocity field; (iii) for narrow windows (σz < 0.03), it is almost completely uncorrelated to the projected galaxy angular density field under the same redshift window; and (iv) it is largely unaffected by multiplicative and additive systematic errors on the observed number of galaxies that are redshift-independent over ~σz. We conclude that $\delta z (\hat{\mathbf {n}})$ is a simple and robust tomographic measure of the cosmic density and velocity fields, complementary to angular clustering, that will contribute to more complete exploitations of current and upcoming galaxy redshift surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Impact of survey spatial variability on galaxy redshift distributions and the cosmological 3 × 2-point statistics for the Rubin Legacy Survey of Space and Time (LSST)

We investigate the impact of spatial survey non-uniformity on the galaxy redshift distributions for forthcoming data releases of the Rubin Observatory Legacy Survey of Space and Time (LSST). Specifically, we construct a mock photometry data set degraded by the Rubin OpSim observing conditions, and estimate photometric redshifts of the sample using a template-fitting photo-z estimator, BPZ, and a machine learning method, FlexZBoost. We select the Gold sample, defined as $i\lt 25.3$ for 10 yr LSST data, with an adjusted magnitude cut for each year and divide it into five tomographic redshift bins for the weak lensing lens and source samples. We quantify the change in the number of objects, mean redshift, and width of each tomographic bin as a function of the coadd i-band depth for 1-yr (Y1), 3-yr (Y3), and 5-yr (Y5) data. In particular, Y3 and Y5 have large non-uniformity due to the rolling cadence of LSST, hence provide a worst-case scenario of the impact from non-uniformity. We find that these quantities typically increase with depth, and the variation can be $10\!-\!40~{{\rm per\ cent}}$ at extreme depth values. Using Y3 as an example, we propagate the variable depth effect to the weak lensing $3\times 2$ pt analysis, and assess the impact on cosmological parameters via a Fisher forecast. We find that galaxy clustering is most susceptible to variable depth, and non-uniformity needs to be mitigated below 3 per cent to recover unbiased cosmological constraints. There is little impact on galaxy–shear and shear–shear power spectra, given the expected LSST Y3 noise.

cosmology↗
DOE OSTI2021

Evidence for galaxy assembly bias in BOSS CMASS redshift-space galaxy correlation function

ABSTRACT Building accurate and flexible galaxy–halo connection models is crucial in modelling galaxy clustering on non-linear scales. Recent studies have found that halo concentration by itself cannot capture the full galaxy assembly bias effect and that the local environment of the halo can be an excellent indicator of galaxy assembly bias. In this paper, we propose an extended halo occupation distribution (HOD) model that includes both a concentration-based assembly bias term and an environment-based assembly bias term. We use this model to achieve a good fit (χ2/degrees of freedom = 1.35) on the 2D redshift-space two-point correlation function (2PCF) of the Baryon Oscillation Spectroscopic Survey (BOSS) CMASS galaxy sample. We find that the inclusion of both assembly bias terms is strongly favoured by the data and the standard five-parameter HOD model is strongly rejected. More interestingly, the redshift-space 2PCF drives the assembly bias parameters in a way that preferentially assigns galaxies to lower mass haloes. This results in galaxy–galaxy lensing predictions that are within 1σ agreement with the observation, alleviating the perceived tension between galaxy clustering and lensing. We also showcase a consistent 3σ–5σ preference for a positive environment-based assembly bias that persists over variations in the fit. We speculate that the environmental dependence might be driven by underlying processes such as mergers and feedback, but might also be indicative of a larger halo boundaries such as the splashback radius. Regardless, this work highlights the importance of building flexible galaxy–halo connection models and demonstrates the extra constraining power of the redshift-space 2PCF.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

ELECTRONIC STRUCTURE METHODS AND PROTOCOLS WITH APPLICATION TO DYNAMICS, KINETICS AND THERMOCHEMISTRY

Hydrocarbon combustion involves the reaction dynamics of a tremendous number of species beginning with many-component fuel mixtures and proceeding via a complex system of intermediates to form primary and secondary products. Combustion conditions corresponding to new advanced engines and/or alternative fuels rely increasingly on autoignition and low-temperature-combustion chemistry. In these regimes various transient radical species such as HO2, ROO·, ·QOOH, HCO, NO2, HOCO, and Criegee intermediates play important roles in determining the detailed as well as more general dynamics. A clear understanding and accurate representation of these processes is needed for effective modeling. Given the difficulties associated with making reliable experimental measurements of these systems, computation can play an important role in developing these energy technologies. Accurate calculations have their own challenges since even within the simplest dynamical approximations such as transition state theory, the rates depend exponentially on critical barrier heights and these may be sensitive to the level of quantum chemistry. Moreover, it is well-known that in many cases it is necessary to go beyond statistical theories and consider the dynamics. Quantum tunneling, resonances, radiative transitions, and non-adiabatic effects governed by spin-orbit or derivative coupling can be determining factors in those dynamics. Building upon progress made during a period of prior support through the DOE Early Career Program, this project combines developments in the areas of potential energy surface (PES) fitting and multistate multireference quantum chemistry to allow spectroscopically and dynamically/kinetically accurate investigations of key molecular systems (such as those mentioned above), many of which are radicals with strong multireference character and have the possibility of multiple electronic states contributing to the observed dynamics. An ongoing area of investigation is to develop general strategies for robustly convergent electronic structure theory for global multichannel reactive surfaces including diabatization of energy and other relevant surfaces such as dipole transition. Combining advances in ab initio methods with automated interpolative PES fitting allows the construction of high-quality PESs (incorporating thousands of high-level data) to be done rapidly through parallel processing on high-performance computing (HPC) clusters. In addition, new methods and approaches to electronic structure theory will be developed and tested through applications. This project will explore limitations in traditional multireference calculations (e.g., MRCI) such as those imposed by internal contraction, lack of high-order correlation treatment and poor scaling. Methods such as DMRG-based extended active-space CASSCF and various Quantum Monte Carlo (QMC) methods will be applied (including VMC/DMC and FCIQMC). Insight into the relative significance of different orbital spaces and the robustness of application of these approaches on leadership class computing architectures will be gained. Synergy with other components of this research program such as automated PES fitting and multireference quantum chemistry will be used to address challenges encountered by the standard approaches to computational thermochemistry (those being single-reference quantum chemistry and perturbative treatments of the anharmonic vibrational energy, which break down for some cases of electronic structure or floppy strongly coupled vibrational modes).

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

Uncertainty in Synthetic Tropical Cyclone Hazard and Risk Estimates: Insights from RAFT, CHAZ, MIT, STORM, and CLIMADA

We synthesize five complementary tropical cyclone (TC) hazard frameworks—RAFT (physics-based machine learning), CHAZ and MIT (statistical–dynamical), STORM (fully statistical), and CLIMADA (observation-driven resampling)—to characterize uncertainty in wind-related TC metrics relevant to energy applications. All datasets and the IBTrACS observational record are harmonized to a common 6-hourly, 2.5° grid. We compare basin-wide and coastal properties using consistent definitions for TC frequency, mean and maximum intensity, 24-hour intensification, and 6-hour translation speed, and quantify agreement with Pearson r, RMSE, and Kling–Gupta efficiency (KGE) alongside resampling-based confidence intervals. CLIMADA is included for basin context but excluded from coastal skill scoring because it resamples historical IBTrACS; if supplied with projected future tracks from an external hazard model, CLIMADA can be used to simulate future TC scenarios. Results show robust, cross-model signals: (i) a corridor of activity from the tropical Atlantic through the Caribbean into the Bahamas and western subtropical Atlantic; (ii) a meridional dipole in 24-hour intensification (low-latitude strengthening, subtropical weakening); and (iii) a transition from slower tropical motion to faster midlatitude translation. Coastal winds (mean and maximum) consistently cluster from the eastern Gulf into the Bahamas–western Atlantic transition. The largest structural spread occurs in the amplitude and footprint of lifetime maximum intensity and, secondarily, in translation speed; intensification exhibits similar central behavior across frameworks with variability in extremes. Translation speed shows the most uniform coastal agreement. These findings provide a decision envelope for wind-focused risk screening and clarify where uncertainty should be carried forward; wind-only results represent a lower bound on total hazard, motivating integration of surge and rainfall modules and a companion, asset-level damage analysis.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Star-spots and magnetism: testing the activity paradigm in the Pleiades and M67

We measure star-spot filling fractions for 240 stars in the Pleiades and M67 open star clusters using APOGEE high-resolution H-band spectra. For this work, we developed a modified spectroscopic pipeline which solves for star-spot filling fraction and star-spot temperature contrast. We exclude binary stars, finding that the large majority of binaries in these clusters (80 per cent) can be identified from Gaia DR3 and APOGEE criteria – important for field star applications. Our data agree well with independent activity proxies, indicating that this technique recovers real star-spot signals. In the Pleiades, filling fractions saturate at a mean level of 0.248 ± 0.005 for active stars with a decline at slower rotation; we present fitting functions as a function of Rossby number. In M67, we recover low mean filling fractions of 0.030 ± 0.008 and 0.003 ± 0.002 for main sequence GK stars and evolved red giants, respectively, confirming that the technique does not produce spurious spot signals in inactive stars. Star-spots also modify the derived spectroscopic effective temperatures and convective overturn time-scales. Effective temperatures for active stars are offset from inactive ones by −109 ± 11 K, in agreement with the Pecaut & Mamajek empirical scale. Star-spot filling fractions at the level measured in active stars changes their inferred overturn time-scale, which biases the derived threshold for saturation. Finally, we identify a population of stars statistically discrepant from mean activity–Rossby relations and present evidence that these are genuine departures from a Rossby scaling. Our technique is applicable to the full APOGEE catalogue, with broad applications to stellar, galactic, and exoplanetary astrophysics.

79 ASTRONOMY AND ASTROPHYSICS↗